Reputation: 7567
I'm trying to follow the method for inserting a Panda data frame into SQL Server that is mentioned here as it appears to be the fastest way to import lots of rows.
However I am struggling with figuring out the connection parameter. I am not using DSN , I have a server name, a database name, and using trusted connection (i.e. windows login).
import sqlalchemy
import urllib
server = 'MYServer'
db = 'MyDB'
cxn_str = "DRIVER={SQL Server Native Client 11.0};SERVER=" + server +",1433;DATABASE="+db+";Trusted_Connection='Yes'"
#cxn_str = "Trusted_Connection='Yes',Driver='{ODBC Driver 13 for SQL Server}',Server="+server+",Database="+db
params = urllib.parse.quote_plus(cxn_str)
engine = sqlalchemy.create_engine("mssql+pyodbc:///?odbc_connect=%s" % params)
conn = engine.connect().connection
cursor = conn.cursor()
I'm just not sure what the correct way to specify my connection string is. Any suggestions?
Upvotes: 1
Views: 3723
Reputation: 2411
I have been working with pandas and SQL server for a while and the fastest way I found to insert a lot of data in a table was in this way:
You can create a temporary CSV using:
df.to_csv('new_file_name.csv', sep=',', encoding='utf-8')
Then use pyobdc
and BULK INSERT
Transact-SQL:
import pyodbc
conn = pyodbc.connect(DRIVER='{SQL Server}', Server='server_name', Database='Database_name', trusted_connection='yes')
cur = conn.cursor()
cur.execute("""BULK INSERT table_name
FROM 'C:\\Users\\folders path\\new_file_name.csv'
WITH
(
CODEPAGE = 'ACP',
FIRSTROW = 2,
FIELDTERMINATOR = ',',
ROWTERMINATOR = '\n'
)""")
conn.commit()
cur.close()
conn.close()
Then you can delete the file:
import os
os.remove('new_file_name.csv')
It was a second to charge a lot of data at once into SQL Server. I hope this gives you an idea.
Note: don't forget to have a field for the index. It was my mistake when I started to use this lol.
Upvotes: 2
Reputation: 123654
Connection string parameter values should not be enclosed in quotes so you should use Trusted_Connection=Yes
instead of Trusted_Connection='Yes'
.
Upvotes: 1